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Bioinformatics Advance Access originally published online on June 16, 2005
Bioinformatics 2005 21(18):3604-3609; doi:10.1093/bioinformatics/bti542
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

The predictive power of the CluSTr database

Robert Petryszak , Ernst Kretschmann , Daniela Wieser and Rolf Apweiler *

EMBL Outstation Hinxton, The European Bioinformatics Institute (EBI) Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK

*To whom correspondence should be addressed.

Summary: The CluSTr database employs a fully automatic single-linkage hierarchical clustering method based on a similarity matrix. In order to compute the matrix, first all-against-all pair-wise comparisons between protein sequences are computed using the Smith–Waterman algorithm. The statistical significance of the similarity scores is then assessed using a Monte Carlo analysis, yielding Z-values, which are used to populate the matrix. This paper describes automated annotation experiments that quantify the predictive power and hence the biological relevance of the CluSTr data. The experiments utilized the UniProt data-mining framework to derive annotation predictions using combinations of InterPro and CluSTr. We show that this combination of data sources greatly increases the precision of predictions made by the data-mining framework, compared with the use of InterPro data alone. We conclude that the CluSTr approach to clustering proteins makes a valuable contribution to traditional protein classifications.

Availability: http://www.ebi.ac.uk/clustr/

Contact: rolf.apweiler{at}ebi.ac.uk

Received on April 5, 2005; revised on June 14, 2005; accepted on June 14, 2005

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